Abstract

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

Received 22 January 2013Accepted 28 May 2013Published online 13 June 2013

Lead Paragraph: Synthetic biology aims to engineer synthetic genetic circuits to endow cells and organisms with the ability to address new applications, including the production of drugs and industrial products, the design of new therapeutics for human diseases, and the study of basic biological processes. Despite significant advances in the field over the last decade, the synthetic biology design cycle has been hampered by the lack of predictive models. In contrast, more established engineering disciplines, such as civil and electrical engineering can rely on rational design by using models that can capture the behavior of real-world systems with a high degree of accuracy. This is currently not the case for synthetic biology, as models are generally only able to make qualitative predictions about system behavior. As a result, producing a functional engineered biological system usually requires extensive manual tuning by trial-and-error, a time-consuming process that significantly slows the biological design process. The lack of predictive power in current models is partly because these models account for only the synthetic circuits themselves, but not other ongoing processes in the cells in which they reside. However, the complex dynamics of synthetic circuits may affect the host cell and vice versa, leading to divergence between current models and experimental results. Recently, a whole-cell model of a small and simple bacterium was developed and shown to capture numerous aspects of this organism's behavior. Here, we adapt this model to enable the easy incorporation of synthetic circuits and investigate its use for design in synthetic biology. We show that synthetic circuits can be integrated into the model and demonstrate the effects that synthetic genes can have on the host cell. We anticipate that this whole-cell modeling approach for synthetic gene circuits will enable more predictive and rational design for the field of synthetic biology.

Acknowledgments:

We would like to thank Vladimir Potapov and Amy Keating for their help with running simulations on their cluster (supported by National Science Foundation under Grant No. 0821391). We thank Jacob Rubens for helpful discussion regarding our results on codon optimization. Bonny Jain was supported by the MIT Electrical Engineering and Computer Science Advanced Undergraduate Research Program, Oliver Purcell was supported by the Defence Advanced Research Projects Agency (DARPA), Jonathan Karr was supported by a NSF Graduate fellowship, Markus Covert was supported by an NIH Director's Pioneer Award (8DP1LM011510-04), and Timothy Lu was supported by an NIH New Innovator Award (1DP2OD008435) and the National Science Foundation (1124247).

Abstract

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.